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          <h1 class="post-title" itemprop="name headline">机器学习和深度学习</h1>
        

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        <p>什么是深度学习</p>
<p>深度学习，如深度神经网络、卷积神经网络和递归神经网络已被应用</p>
<p>计算机视觉、语音识别、自然语言处理、音频识别与生物信息学等领域</p>
<p>并获取了极好的效果。</p>
<p><img src="/2018/11/30/机器学习和深度学习/clipboard.png" alt="img"></p>
<a id="more"></a>
<p>解决什么样的问题呢？</p>
<p>比如：最常见的就是，图像识别、自然语言的处理</p>
<p>当然了，前面机器学习中的功能也是能够实现，而且能够实现的更好</p>
<p>简单来讲机器学习和深度学习的不同就是算法不同，效果可能不同而已，为了区别于通常意义上的机器学习，取了个名字就是深度学习</p>
<p>当然深度学习也有对应的架构来帮助我们来解决一些实际应用领域的问题</p>
<p><img src="/2018/11/30/机器学习和深度学习/clipboard2.png" alt="img"></p>
<p>其中可能是最牛的可能就是Tensorflow</p>
<p>开源啊，再次膜拜Google</p>
<p>对比其他架构</p>
<p><img src="/2018/11/30/机器学习和深度学习/clipboard3.png" alt="img"></p>
<p>起源与变更</p>
<p><img src="/2018/11/30/机器学习和深度学习/clipboard4.png" alt="img"></p>
<p>谷歌大脑开始是谁带领带领的呢？吴恩达！</p>
<p>吊不吊，名声如雷贯耳的围棋阿尔法狗也是用这个实现的。</p>
<p>这么厉害的一个人，加入百度以后，唉！！！！！！</p>
<p>让大数据和深度学习可以正式有架构支撑的人Jeff Dean，Hadoop来自于谷歌一款名为MapReduce的编程模型包。现在这个人主导了这个架构的诞生。</p>
<p>就问Google吊不吊，关键这些东西要么就直接开源，要么就是给出了可执行的方案。</p>
<p>“Dont be evil” 不作恶，真的不是说说而已。</p>
<p>最后插句嘴，SB甲骨文。</p>
<p>Tensorflow的优点</p>
<p>1、真正的可移植性</p>
<p>引入各种计算设备的支持包括CPU/GPU/TPU，以及能够很好地运行在移动端，</p>
<p>如安卓设备、ios、树莓派等等</p>
<p>2、多语言支持</p>
<p>Tensorflow 有一个合理的c++使用界面，也有一个易用的python使用界面来构建和</p>
<p>执行你的graphs，你可以直接写python/c++程序。</p>
<p>3、高度的灵活性与效率</p>
<p>TensorFlow是一个采用数据流图（data flow graphs），用于数值计算的开源软件库</p>
<p>能够灵活进行组装图，执行图。随着开发的进展，Tensorflow的效率不算在提高</p>
<p>4、支持TensorFlow 由谷歌提供支持，谷歌投入了大量精力开发 TensorFlow，它希望 </p>
<p>TensorFlow 成为机器学习研究人员和开发人员的通用语言</p>
<p>地址：<a href="https://tensorflow.google.cn/" target="_blank" rel="noopener">https://tensorflow.google.cn/</a></p>
<p>分享一下自己的笔记地址：<a href="https://note.youdao.com/share/?id=b8ffe42d3b1efa584a2a8de3c7539017&amp;type=notebook#/" target="_blank" rel="noopener">https://note.youdao.com/share/?id=b8ffe42d3b1efa584a2a8de3c7539017&amp;type=notebook#/</a></p>

      
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